Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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Improved Decision Tree Algorithms by Considering Variables Interaction

교호효과를 고려한 향상된 의사결정나무 알고리듬에 관한 연구

  • Kwon, Keunseob (Department of Industrial Engineering, Hanyang University) ;
  • Choi, Gyunghyun (Department of Industrial Engineering, Hanyang University)
  • Published : 2004.12.31
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Abstract

Much of previous attention on researches of the decision tree focuses on the splitting criteria and optimization of tree size. Nowadays the quantity of the data increase and relation of variables becomes very complex. And hence, this comes to have plenty number of unnecessary node and leaf. Consequently the confidence of the explanation and forecasting of the decision tree falls off. In this research report, we propose some decision tree algorithms considering the interaction of predictor variables. A generic algorithm, the k-1 Algorithm, dealing with the interaction with a combination of all predictor variable is presented. And then, the extended version k-k Algorithm which considers with the interaction every k-depth with a combination of some predictor variables. Also, we present an improved algorithm by introducing control parameter to the algorithms. The algorithms are tested by real field credit card data, census data, bank data, etc.

Keywords

References

  1. Brieman, L., Friedman, J. H., Olshen, R. A. & Stone, C. J.(1984), Classification and Regression Trees, Wadsorth
  2. Fayyad, U. M., Piatesky, G. Shapiro and Smith, P.(1996), From Data Mining to Knowledge Discovery: an overview. In Fayyad, U.M. et al. (Eds) Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1-34
  3. Kass, G.(1980), An exploratory technique for investigating large quantities of categorical data, Applied Statistics
  4. Quinlan, J. R.(1986), Induction of Decision Trees. Machine Learning, 1(1)
  5. Quinlan, J. R(1993), C4.5: Programs for Machine Learning, San Mateo, Calif., Morgan Kanfmann
  6. Quinlan, J. R(1996)., Bagging, Boosting, and C4.5, Proceedings of 13th National Conference on Artificial Intelligence, Portland, Oregon, August 4-8
  7. Shlien, S.(1990)., Multiple Binary Decision Tree Classifiers, Pattern Recognition, 23(7), 757-763 https://doi.org/10.1016/0031-3203(90)90098-6
  8. Smyth, P., and Goodman, R.M.(1992), An Information Theoretic approach to Rule Induction From Database. IEEE Transaction on Knowledge and Data Engineering, 4(4), 301-316